Dynamic data-driven decision models for infectious disease control

用于传染病控制的动态数据驱动决策模型

• 批准号: 8703900
• 负责人: ALISON P GALVANI
• 金额: $ 73.09万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8703900
• 关键词: Address; Antibodies; Antiviral Agents; Awareness; Basic Science; Behavior Control; Behavioral; Biological; Biosensing Techniques; Brazil; Budgets; Centers for Disease Control and Prevention (U.S.); Child; Collaborations; Combined Vaccines; Communicable Diseases; Complex; Connecticut; Cost Effectiveness Analysis; Coupling; Culicidae; Data; Data Sources; Decision Making; Decision Modeling; Dengue; Detection; Disease; Disease Outbreaks; Early Diagnosis; Economics; Ecosystem; Electronic Health Record; Epidemic; Evaluation; Funding; Future; Game Theory; Goals; Grant; Health; Health Personnel; Health Services; Influenza; Injection of therapeutic agent; Institute of Medicine (U.S.); Interdisciplinary Study; Internet; Intervention; Israel; Louisiana; Measures; Methods; Modeling; Molecular; Monitor; Online Systems; Pertussis; Policies; Population Dynamics; Population Surveillance; Procedures; Prophylactic treatment; Public Health; Rabies; Readiness; Resources; Respiratory syncytial virus; Schedule; Science; Source; System; Texas; Time; Translations; Uncertainty; Vaccines; Vector-transmitted infectious disease; Ventilator; Viral; West Nile virus; Work; authority; base; data modeling; design; disease transmission; disorder control; disorder prevention; fight against; flu; high risk; improved; infectious disease model; innovation; insight; mathematical model; network models; next generation sequencing; novel; novel strategies; pandemic influenza; pandemic preparedness; public health relevance; response; seasonal influenza; social; surveillance strategy; tool; transmission process; trend; user friendly software; user-friendly; vector control

DESCRIPTION (provided by applicant): We aim to improve infectious disease surveillance and control through mathematical modeling, optimization, and translational collaborations with public health decision makers. Methodologically, we will advance the application of mathematical modeling to inform public heath policy decisions by (i) integrating large-scale optimization, economic analyses, and uncertainty quantification into mathematical models of disease transmission in complex and dynamic populations, and by (ii) developing goal-oriented optimization methods for integrating diverse data sources to improve infectious disease surveillance systems. We will apply these approaches using data on influenza, respiratory syncytial virus (RSV), pertussis, West Nile virus (WNV), and dengue from around the world to elucidate the complex drivers of outbreaks and control and to identify highly effective, economical, and feasible control policies. We will disseminate our models and results to public health authorities and develop user-friendly modeling tools to facilitate preparedness and real-time decision- making regarding the optimal distribution of limited disease control resources. Thus, our interdisciplinary research will expand the methodological toolkit for modeling infectious disease dynamics, provide better strategies for tracking and mitigating epidemics, and make science, data, and models more broadly accessible to public health agencies engaged in the global fight against infectious diseases.

描述（由申请人提供）：我们的目标是通过数学建模，优化和与公共卫生决策者的翻译合作来改善传染病监测和控制。在方法上，我们将推进数学建模的应用，通过（i）将大规模优化，经济分析和不确定性量化集成到复杂和动态人群中疾病传播的数学模型中，以及（ii）开发目标导向的优化方法，用于整合不同的数据源，以改善传染病监测系统。我们将应用这些方法，使用来自世界各地的流感，呼吸道合胞病毒（RSV），百日咳，西尼罗河病毒（WNV）和登革热的数据来阐明爆发和控制的复杂驱动因素，并确定高效，经济和可行的控制政策。 我们将向公共卫生当局传播我们的模型和结果，并开发用户友好的建模工具，以促进有关有限疾病控制资源优化分配的准备和实时决策。因此，我们的跨学科研究将扩大传染病动态建模的方法工具包，提供更好的跟踪和缓解流行病的策略，并使科学，数据和模型更广泛地提供给参与全球抗击传染病的公共卫生机构。